A method of moments estimator of tail dependence

dc.creatorEinmahl, John H. J.
dc.creatorKrajina, Andrea
dc.creatorSegers, Johan
dc.date2007-10-10
dc.date2008-11-14
dc.date.accessioned2026-07-07T10:17:50Z
dc.date.available2026-07-07T10:17:50Z
dc.descriptionIn the world of multivariate extremes, estimation of the dependence structure still presents a challenge and an interesting problem. A procedure for the bivariate case is presented that opens the road to a similar way of handling the problem in a truly multivariate setting. We consider a semi-parametric model in which the stable tail dependence function is parametrically modeled. Given a random sample from a bivariate distribution function, the problem is to estimate the unknown parameter. A method of moments estimator is proposed where a certain integral of a nonparametric, rank-based estimator of the stable tail dependence function is matched with the corresponding parametric version. Under very weak conditions, the estimator is shown to be consistent and asymptotically normal. Moreover, a comparison between the parametric and nonparametric estimators leads to a goodness-of-fit test for the semiparametric model. The performance of the estimator is illustrated for a discrete spectral measure that arises in a factor-type model and for which likelihood-based methods break down. A second example is that of a family of stable tail dependence functions of certain meta-elliptical distributions.
dc.descriptionPublished in at http://dx.doi.org/10.3150/08-BEJ130 the Bernoulli (http://isi.cbs.nl/bernoulli/) by the International Statistical Institute/Bernoulli Society (http://isi.cbs.nl/BS/bshome.htm)
dc.identifierhttps://arxiv.org/abs/0710.2039
dc.identifierhttp://arxiv.org/abs/0710.2039
dc.identifierBernoulli 2008, Vol. 14, No. 4, 1003-1026
dc.identifierdoi:10.3150/08-BEJ130
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/173990
dc.subjectStatistics Theory
dc.titleA method of moments estimator of tail dependence
dc.typetext

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